"""Baseline validation matrix: sweep configs x pressures x speeds."""

from typing import List, Optional
import pandas as pd

from cryosim import predict


CONFIGS = ["old_icv", "new_icv"]
PRESSURES = [50.0, 100.0, 200.0, 350.0, 500.0, 700.0, 900.0]
SPEEDS = [0.65, 0.8, 1.0]


def run_validation_matrix(
    configs: Optional[List[str]] = None,
    pressures: Optional[List[float]] = None,
    speeds: Optional[List[float]] = None,
    flash_eff: float = 0.0,
) -> pd.DataFrame:
    """Run the engine across a grid of conditions.

    Returns DataFrame with one row per (config, pressure, speed) combination.
    """
    configs = configs or CONFIGS
    pressures = pressures or PRESSURES
    speeds = speeds or SPEEDS

    rows = []
    for cfg_name in configs:
        for Pexit in pressures:
            for speed in speeds:
                try:
                    r = predict(
                        hardware=cfg_name,
                        Pexit=Pexit,
                        speed=speed,
                        flash_eff=flash_eff,
                    )
                    rows.append({
                        "config": cfg_name,
                        "Pexit": Pexit,
                        "speed": speed,
                        "mdot_kgpm": r.mdot_kgpm,
                        "mass_eff": r.mass_eff,
                        "Tc_peak_K": r.Tc_peak_K,
                        "pc_peak_barg": r.pc_peak_barg,
                        "DCV_ct_s": r.DCV_ct_s,
                        "ICV_open_frac": r.ICV_open_frac,
                        "kWh_extend": r.kWh_extend,
                        "cpu_s": r.cpu_s,
                        "steps": r.steps,
                        "error": None,
                    })
                except Exception as e:
                    rows.append({
                        "config": cfg_name,
                        "Pexit": Pexit,
                        "speed": speed,
                        "mdot_kgpm": None,
                        "mass_eff": None,
                        "Tc_peak_K": None,
                        "pc_peak_barg": None,
                        "DCV_ct_s": None,
                        "ICV_open_frac": None,
                        "kWh_extend": None,
                        "cpu_s": None,
                        "steps": None,
                        "error": str(e),
                    })

    return pd.DataFrame(rows)


def save_matrix(df: pd.DataFrame, path: str = "validation_matrix.csv"):
    """Save validation matrix results to CSV."""
    df.to_csv(path, index=False)
    print(f"Saved {len(df)} results to {path}")


if __name__ == "__main__":
    import time
    print("Running full validation matrix...")
    print(f"  Configs: {CONFIGS}")
    print(f"  Pressures: {PRESSURES}")
    print(f"  Speeds: {SPEEDS}")
    print(f"  Total cases: {len(CONFIGS) * len(PRESSURES) * len(SPEEDS)}")
    print()

    t0 = time.time()
    df = run_validation_matrix()
    elapsed = time.time() - t0

    print(f"\nCompleted in {elapsed:.1f}s")
    print(f"Failures: {df['error'].notna().sum()}")
    print()
    print(df[["config", "Pexit", "speed", "mdot_kgpm", "mass_eff", "cpu_s"]].to_string(index=False))

    save_matrix(df)